Due to ageing of the population the incidence of multimorbidity and polypharmacy is rising. Polypharmacy is a risk factor for medication-related (re)admission and therefore places a significant ...burden on the healthcare system. The reported incidence of medication-related (re)admissions varies widely due to the lack of a clear definition. Some medications are known to increase the risk for medication-related admission and are therefore published in the triggerlist of the Dutch guideline for Polypharmacy in older patients. Different interventions to support medication optimization have been studied to reduce medication-related (re)admissions. However, the optimal template of medication optimization is still unknown, which contributes to the large heterogeneity of their effect on hospital readmissions. Therefore, we implemented a clinical decision support system (CDSS) to optimize medication lists and investigate whether continuous use of a CDSS reduces the number of hospital readmissions in older patients, who previously have had an unplanned probably medication-related hospitalization.
The CHECkUP study is a multicentre randomized study in older (≥60 years) patients with an unplanned hospitalization, polypharmacy (≥5 medications) and using at least two medications from the triggerlist, from Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. Patients will be randomized. The intervention consists of continuous (weekly) use of a CDSS, which generates a Medication Optimization Profile, which will be sent to the patient's general practitioner and pharmacist. The control group will receive standard care. The primary outcome is hospital readmission within 1 year after study inclusion. Secondary outcomes are one-year mortality, number of emergency department visits, nursing home admissions, time to hospital readmissions and we will evaluate the quality of life and socio-economic status.
This study is expected to add evidence on the knowledge of medication optimization and whether use of a continuous CDSS ameliorates the risk of adverse outcomes in older patients, already at an increased risk of medication-related (re)admission. To our knowledge, this is the first large study, providing one-year follow-up data and reporting not only on quality of care indicators, but also on quality-of-life.
The trial was registered in the Netherlands Trial Register on October 14, 2018, identifier: NL7449 (NTR7691). https://www.trialregister.nl/trial/7449 .
Falls are the leading cause of injury-related mortality and hospitalization among adults aged greater than or equal to 65 years. An important modifiable fall-risk factor is use of fall-risk ...increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs. A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged greater than or equal to 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression. The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries.
Since the outbreak of SARS‐CoV‐2, also known as COVID‐19, conflicting theories have circulated on the influence of angiotensin‐converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers ...(ARB) on incidence and clinical course of COVID‐19, but data are scarce. The COvid MEdicaTion (COMET) study is an observational, multinational study that focused on the clinical course of COVID‐19 (i.e. hospital mortality and intensive care unit ICU admission), and included COVID‐19 patients who were registered at the emergency department or admitted to clinical wards of 63 participating hospitals. Pharmacists, clinical pharmacologists or treating physicians collected data on medication prescribed prior to admission. The association between the medication and composite clinical endpoint, including mortality and ICU admission, was analysed by multivariable logistic regression models to adjust for potential confounders. A total of 4870 patients were enrolled. ACEi were used by 847 (17.4%) patients and ARB by 761 (15.6%) patients. No significant association was seen with ACEi and the composite endpoint (adjusted odds ratio OR 0.94; 95% confidence interval CI 0.79 to 1.12), mortality (OR 1.03; 95%CI 0.84 to 1.27) or ICU admission (OR 0.96; 95%CI 0.78 to 1.19) after adjustment for covariates. Similarly, no association was observed between ARB and the composite endpoint (OR 1.09; 95%CI 0.90 to 1.30), mortality (OR 1.12; OR 0.90 to 1.39) or ICU admission (OR 1.21; 95%CI 0.98 to 1.49). In conclusion, we found no evidence of a harmful or beneficial effect of ACEi or ARB use prior to hospital admission on ICU admission or hospital mortality.
Background
Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing ...and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.
Objective
The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies.
Methods
Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts ‘handled’ by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated.
Results
A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (
p
< 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (
p
< 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%,
p
< 0.001 day 1; 29.8% vs 19.4%,
p
< 0.001 day 3).
Conclusion
This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. As such, our study provides an important insight into the spontaneous course of resolved alerts, since these data are currently lacking in the literature.
Various theories about drugs such as ACE inhibitors or angiotensin II receptor blockers (ARBs) in relation to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and clinical outcomes of ...COVID-19 are circulating in both mainstream media and medical literature. These are based on the fact that ACE2 facilitates SARS-CoV-2 cell invasion via binding of a viral spike protein to ACE2. However, the effect of ACE inhibitors, ARBs and other drugs on ACE2 is unclear and all theories are based on conflicting evidence mainly from animal studies. Therefore, clinical evidence is urgently needed. The aim of this study is to investigate the relationship between use of these drugs on clinical outcome of patients with COVID-19. Patients will be included from several hospitals in Europe. Data will be collected in a user-friendly database (Digitalis) on an external server. Analyses will be adjusted for sex, age and presence of cardiovascular disease, hypertension and diabetes. These results will enable more rational choices for randomised controlled trials for preventive and therapeutic strategies in COVID-19.
Evidence on the association between single nucleotide polymorphisms (SNPs) in the vitamin D receptor (VDR) and depressive symptoms is inconclusive.
The primary aim of the study was to investigate the ...association between SNPs in the VDR gene and depressive symptoms.
In a sample of older adults from the Longitudinal Ageing Study Amsterdam (n = 922), depressive symptoms were assessed using the Centre for Epidemiological Studies Depression scale (CES-D scale) at baseline and after 3, 6, and 10 y of follow-up. Blood samples for SNP and serum 25-hydroxyvitamin D3 (25(OH)D3) determination were obtained at baseline. The association between 13 SNPs in the VDR gene and the course of depressive symptoms were evaluated using linear mixed models. The interaction between SNPs and serum 25(OH)D3 in relation to depressive symptoms was evaluated using multiple linear regression.
No SNPs were associated with the course of depressive symptoms. Significant interactions between serum 25(OH)D3 and SNPs in the VDR gene were found. Stratified analysis revealed that within the GG genotype strata, 10 nmol/L higher serum 25(OH)D3 was associated with 0.27 (95% CI: −0.50, −0.04) and 0.23 (95% CI: −0.48, 0.02) lower scores on the CES-D scale for Cdx-2 and 1b-G-886A, respectively. This association was not found in persons having the GA or AA genotype.
No SNPs are associated with the course of depressive symptoms. Stratified analysis shows that the effect of serum 25(OH)D3 concentrations on depressive symptoms is different among genotypes of Cdx-2 and 1b-G-886A. Future research should elucidate on the function of Cdx-2 and 1b-G-886A to describe their effect.
By preparing and empowering patients prior to a consultation, Patient Portals can be powerful tools to stimulate patient participation in Shared Decision Making (SDM) about falls prevention for older ...adults. However, previous research shows that in developing these portals patients’ needs are often overlooked, problematically leading to ineffective portals. We developed a Patient Portal as part of a multi-component intervention (including a physician component) that takes the end-users’ needs and preferences into account by incorporating User-Centered Design (UCD). The aim of this study is to test the usability of a patient portal that is developed according to the UCD principles.
The Patient Portal was tested through a concurrent think a-loud usability test, a think a-loud questionnaire (i.e. Website Satisfaction Scale) using a scale visually supported by smiley faces, and a semi-structured interview focused on Usability, Content, Navigation, and Comprehensibility (n=6 patients). The final dataset consisted of videotaped screen recordings including mouse movements and clicks, video recordings of the patients, and the interview audio recordings. The occurrence and severity of the usability problems were coded according to Nielsen’s severity rating (0-4).
In total, n=41 usability problems were identified. Most problems related to the Portal’s Usability and Navigation (e.g. the font size was considered to be too small, difficulty locating a navigation button in the menu bar). Problems regarding comprehensibility were also found (e.g. difficult words required an explanation or easier to understand alternative).
Based on the results, the Patient Portal was improved into a ready-to-use portal. Only minor edits were made, possibly because end-users were involved in the development from the start. A Clinical Decision Support System for physicians was simultaneously developed as part of the ADFICE_IT intervention. Next, an RCT with process evaluation will be performed across eight Dutch clinics to study the effects of the intervention.
Abstract Background Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). ...We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. Methods Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. Results We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 interquartile range (IQR) 426–2766 versus 90 441 (IQR 56 442–128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5–11); for RCD-based models, it was 16 (IQR 11–26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. Conclusions Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.
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